Various types of biometric systems are used more and more in order to provide an increased security for accessing an electronic device and at the same time keep the user convenience at an acceptable level. In particular fingerprint sensors have been successfully integrated in such devices, for example, thanks to their small form factor, high performance and user acceptance. Among the various available fingerprint sensing principles (such as capacitive, optical, thermal etc.), capacitive sensing is most commonly used, in particular in applications where size and power consumption are important.
All capacitive fingerprint sensors provide an indicative measure of the capacitance between several sensing elements and a finger placed on the surface of the fingerprint sensor. Acquisition of a fingerprint image is typically performed using a fingerprint sensor comprising a plurality of sensing elements arranged in a two-dimensional manner, and a block based technique may be applied to the fingerprint sensor for acquiring a fingerprint image, where the blocks of sensing elements are sampled sequentially.
One of the problems associated with fingerprint sensors concerns the humidity of the finger which is a fundamental problem in particular for capacitive fingerprint sensors. An increase in the humidity level may saturate the fingerprint image which makes verification difficult and sometimes even impossible. Although the humidity issue is an important issue to handle, there may also be other situations which may lead to similar problems for verification. For example, pressure variations on the fingerprint sensor may cause differences in the pixel response of the capacitive fingerprint sensor. This may negatively affect the image quality and the possibility to extract features from the images. In both of the described situations, it may be possible to improve the situation by for example adjusting the sensitivity level of the fingerprint sensor. Such an approach is described in US2014/0267659 in which it is described capturing several images at different sensitivity level and subsequently combining the images. However, this requires substantial computational power, and is also time consuming, thus negatively affecting e.g. user convenience.
Thus, there is a need for improvements with regards fingerprint authentication with fingerprint data combined from several images.